Based upon the recent work of Dacheng Xiu's team at the University of Chicago, we demonstrate that we can repurpose a consumer-level character recognition neural network to recognize price chart patterns in the Ethereum cryptocurrency for the purpose of forecasting short term (small N days)...

By applying a Heston-like stochastic volatility model to an empirical returns-based drift-diffusion model, we show by extensive backtesting that we can improve Bitcoin price forecast accuracy for investment horizons of 7, 30, 45, and 60 days. In particular, we can improve median forecasts and...

Crypto currency perpetual futures asset price changes seem to have some predictive power for short term (tens of minutes) forecasts of underlying crypto currency price changes. In this study, we apply simple vector auto-regressive models to Bitcoin spot and futures price changes and allow for a...

Returns tail ratio along with low order returns distribution moments seem to be useful for forecasting one day ahead crypto currency returns in some cases. Linear combinations of these distribution metrics showed promise for forecasting. By relying on term-reduced linear regression, we are able...

As machine learning modeling becomes more robust and codified with well known and proven software packages such as SciKit Learn and Tensorflow, the fact remains that serious coding, data cleaning and prepping, and machine learning knowledge is required to use these tools effectively. Here we...